Ash Tree Identification Based on the Integration of Hyperspectral Imagery and High-density Lidar Data

Monitoring and management of ash trees has become particularly important in recent years due to the heightened risk of attack from the invasive pest, the emerald ash borer (EAB). However, distinguishing ash from other deciduous trees can be challenging. Both hyperspectral imagery and Light detection and ranging (LiDAR) data are two valuable data sources that are often used for tree species classification. Hyperspectral imagery measures detailed spectral reflectance related to the biochemical properties of vegetation, while LiDAR data measures the three-dimensional structure of tree crowns related to morphological characteristics. Thus, the accuracy of vegetation classification may be improved by combining both techniques. Therefore, the objective of this research is to integrate hyperspectral imagery and LiDAR data for improving ash tree identification. Specifically, the research aims include: 1) using LiDAR data for individual tree crowns segmentation; 2) using hyperspectral imagery for extraction of relative pure crown spectra; 3) fusing hyperspectral and LiDAR data for ash tree identification. It is expected that the classification accuracy of ash trees will be significantly improved with the integration of hyperspectral and LiDAR techniques. Analysis results suggest that, first, 3D crown structures of individual trees can be reconstructed using a set of generalized geometric models which optimally matched LiDAR-derived raster image, and crown widths can be further estimated using tree height and shape-related parameters as independent variables and ground measurement of crown widths as dependent variables. Second, with constrained linear spectral mixture analysis method, the fractions of all materials within a pixel can be extracted, and relative pure crown-scale spectra can be further calculated using illuminated-leaf fraction as weighting factors for tree species classification. Third, both crown shape index (SI) and coefficient of variation (CV) can be extracted from LiDAR data as invariant variables in tree’s life cycle, and improve ash tree identification by integrating with pixel-weighted crown spectra. Therefore, three major contributions of this research have been made in the field of tree species classification:1) the automatic estimation of individual tree crown width from LiDAR data by combining a generalized geometric model and a regression model, 2) the computation of relative pure crown-scale spectral reflectance using a pixel-weighting algorithm for tree species classification, 3) the fusion of shape-related structural features and pixel-weighted crown-scale spectral features for improving of ash tree identification

Automated Treetop Detection and Tree Crown Identification Using Discrete-return Lidar Data

Accurate estimates of tree and forest biomass are essential for a wide range of applications. Automated treetop detection and tree crown discrimination using LiDAR data can greatly facilitate forest biomass estimation. Previous work has focused on homogenous or single-species forests, while few studies have focused on mixed forests. In this study, a new method for treetop detection is proposed in which the treetop is the cluster center of selected points rather than the highest point. Based on treetop detection, tree crowns are discriminated through comparison of three-dimensional shape signatures. The methods are first tested using simulated LiDAR point clouds for trees, and then applied to real LiDAR data from the Soquel Demonstration State Forest, California, USA. Results from both simulated and real LiDAR data show that the proposed method has great potential for effective detection of treetops and discrimination of tree crowns

Construction of stable Ta3N5/g-C3N4 metal/non-metal nitride hybrids with enhanced visible-light photocatalysis

In this paper, a novel Ta3N5/g-C3N4 metal/non-metal nitride hybrid was successfully synthesized by a facile impregnation method. The photocatalytic activity of Ta3N5/g-C3N4 hybrid nitrides was evaluated by the degradation of organic dye rhodamine B (RhB) under visible light irradiation, and the result indicated that all Ta3N5/g-C3N4 samples exhibited distinctly enhanced photocatalytic activities for the degradation of RhB than pure g-C3N4. The optimal Ta3N5/g-C3N4 composite sample, with Ta3N5 mass ratio of 2%, demonstrated the highest photocatalytic activity, and its degradation rate constant was 2.71 times as high as that of pure g-C3N4. The enhanced photocatalytic activity of this Ta3N5/g-C3N4 metal/metal-free nitride was predominantly attributed to the synergistic effect which increased visible-light absorption and facilitated the efficient separation of photoinduced electrons and holes. The Ta3N5/g-C3N4 hybrid nitride exhibited excellent photostability and reusability. The possible mechanism for improved photocatalytic performance was proposed. Overall, this work may provide a facile way to synthesize the highly efficient metal/metal-free hybrid nitride photocatalysts with promising applications in environmental purification and energy conversion

Vaccaria hypaphorine alleviates lipopolysaccharide-induced inflammation via inactivation of NFκB and ERK pathways in Raw 264.7 cells

10.1186/s12906-017-1635-1BMC Complementary and Alternative Medicine17112

Protective Effect Against Toxoplasmosis in BALB/c Mice Vaccinated With Toxoplasma gondii Macrophage Migration Inhibitory Factor

Toxoplasma gondii is an obligate intracellular parasite responsible for toxoplasmosis, which can cause severe disease in the fetus and immunocompromised individuals. Developing an effective vaccine is crucial to control this disease. Macrophage migration inhibitory factor (MIF) has gained substantial attention as a pivotal upstream cytokine to mediate innate and adaptive immune responses. Homologs of MIF have been discovered in many parasitic species, and one homolog of MIF has been isolated from the parasite Toxoplasma gondii. In this study, the recombinant Toxoplasma gondii MIF (rTgMIF) as a protein vaccine was expressed and evaluated by intramuscular injection in BALB/c mice. We divided the mice into different dose groups of vaccines, and all immunizations with purified rTgMIF protein were performed at 0, 2, and 4 weeks. The protective efficacy of vaccination was analyzed by antibody assays, cytokine measurements and lymphoproliferative assays, respectively. The results obtained indicated that the rTgMIF vaccine elicited strong humoral and cellular immune responses with high levels of IgG antibody and IFN-γ production compared to those of the controls, in addition to slight higher levels of IL-4 production. After vaccination, a stronger lymphoproliferative response was also noted. Additionally, the survival time of mice immunized with rTgMIF was longer than that of the mice in control groups after challenge infection with virulent T. gondii RH tachyzoites. Moreover, the number of brain tissue cysts in vaccinated mice was reduced by 62.26% compared with the control group. These findings demonstrated that recombinant TgMIF protein is a potential candidate for vaccine development against toxoplasmosis

AGI for Agriculture

Artificial General Intelligence (AGI) is poised to revolutionize a variety of sectors, including healthcare, finance, transportation, and education. Within healthcare, AGI is being utilized to analyze clinical medical notes, recognize patterns in patient data, and aid in patient management. Agriculture is another critical sector that impacts the lives of individuals worldwide. It serves as a foundation for providing food, fiber, and fuel, yet faces several challenges, such as climate change, soil degradation, water scarcity, and food security. AGI has the potential to tackle these issues by enhancing crop yields, reducing waste, and promoting sustainable farming practices. It can also help farmers make informed decisions by leveraging real-time data, leading to more efficient and effective farm management. This paper delves into the potential future applications of AGI in agriculture, such as agriculture image processing, natural language processing (NLP), robotics, knowledge graphs, and infrastructure, and their impact on precision livestock and precision crops. By leveraging the power of AGI, these emerging technologies can provide farmers with actionable insights, allowing for optimized decision-making and increased productivity. The transformative potential of AGI in agriculture is vast, and this paper aims to highlight its potential to revolutionize the industry